MétaCan
Menu
Back to cohort

Spin-to-Charge Conversion in Magnetic Weyl Semimetals

2019· article· en· W2939645972 on OpenAlexaff
Steven S.-L. Zhang, A. A. Burkov, Ivar Martin, Olle Heinonen

Bibliographic record

VenuePhysical Review Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversity of Waterloo
FundersAmes LaboratoryDivision of Materials Sciences and EngineeringBasic Energy SciencesOffice of ScienceU.S. Department of Energy
KeywordsSpin (aerodynamics)Condensed matter physicsSemimetalCharge (physics)PhysicsMaterials scienceQuantum mechanicsBand gap

Abstract

fetched live from OpenAlex

Weyl semimetals (WSM) are a newly discovered class of quantum materials which can host a number of exotic bulk transport properties, such as the chiral magnetic effect, negative magnetoresistance, and the anomalous Hall effect. In this work, we investigate theoretically the spin-to-charge conversion in a bilayer consisting of a magnetic WSM and a normal metal (NM), where a charge current can be induced in the WSM by a spin current injection at the interface. We show that the induced charge current exhibits a peculiar anisotropy: it vanishes along the magnetization orientation of the magnetic WSM, regardless of the direction of the injected spin. This anisotropy originates from the unique band structure of magnetic WSMs and distinguishes the spin-to-charge conversion effect in WSM-NM structures from that observed in other systems, such as heterostructures involving heavy metals or topological insulators. The induced charge current depends strongly on injected spin orientation, as well as on the position of the Fermi level relative to the Weyl nodes and the separation between them. These dependencies provide additional means to control and manipulate spin-charge conversion in these topological materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.265
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations38
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Review LettersSame topicTopological Materials and PhenomenaFrench-language works237,207